The present invention relates to imaging target trackers, and more particularly, to a likelihood-based threshold selection method and imaging target tracker using same.
Many gated imaging target trackers use thresholding to distinguish target regions from background areas in the input imagery. The thresholding operation and calculation of geometric moments from the resulting binary image are computationally inexpensive enough to be performed every cycle, allowing the estimate of target position to be updated very frequently.
A typical centroid tracker might employ a threshold in concert with a polarity indicator to separate from the remainder of the image the bright or dark pixels that belong to the target. Extensions of this technique include the use of multiple thresholds, with the target pixels being those whose grey levels fall between or outside the bands specified by the threshold values.
Most threshold selection algorithms find thresholds that are optimal according to specific regional or global statistics. These traditional approaches do not involve any model of the target except that it is expected to be separable from the background by a threshold suite. Thus they fail to make use of the most important unique feature of imaging target trackers: the well-known location of the target in each frame.
The shortcomings of prior art are that most threshold selection algorithms described in the literature are not concerned with segmentation of the image into target and background components. Instead, most are designed to optimize some global statistic, such as entropy, edge prevalence, or visual appearance, for example. Such methods are described by T. Pun, "Entropic Thresholding: A New Approach," Computer Vision, Graphics and Image Processing Vol. 16, pp. 210-239, 1981, R. Kohler, "A Segmentation System Based on Thresholding," Computer Graphics and Image Processing Vol. 15, pp. 319-338, 1981, and R. Whatmough, "Automatic Threshold Selection from a Histogram Using the 'Exponential Hull," CVGIP: Graphical Models and Image Processing Vol. 53, p. 592-600, 1991, respectively. For instance, an approach that attempts to maximize information content might result in a threshold pair like that in FIG. 1(d), yielding a silhouette in which the true target pixels are split between the foreground and background of the resulting binary image. Methods of this nature are not suited for use with centroid trackers that expect the silhouette image to contain only target pixels in its foreground.
Threshold selection algorithms used in most imaging target trackers benefit from knowledge of the target position, and can therefore use metrics of threshold quality that favor extraction of foreground pixels only within the track gate. Additionally, since thresholding occurs only in the region immediately surrounding the track gate, behavior of the thresholds over the remainder of the image does not affect track measurement accuracy.
Despite these advantages, traditional methods of selecting thresholds for imaging trackers have not taken advantage of the fact that the object to be segmented is the same in each frame over time. Standard approaches typically look at each frame separately, using temporal continuity only to prevent the thresholds from changing too rapidly. The traditional methods often assume that the target contrasts with the background at one or the other end of the grey scale dynamic range, and that it maintains this polarity throughout the life of the track. Such approaches are described in U.S. Pat. No, 4,849,906, issued to Chodos et al. and U.S. Pat. No. 4,719,584 issued to Rue et al., for example. These approaches cannot find thresholds to segment out a target if it is neither uniformly brighter nor darker than its background. Furthermore, even if the target was tracked in a dark area of the background where the target contrasts positively against the background, if the target subsequently moved over a bright portion of the background, the positive contrast assumption would be incorrect and loss of lock would likely result.
Therefore, it is an objective of the present invention to provide for an improved threshold selection method and imaging target tracker using same.